Brain Biometrics under Auditory Stimulation for Human Identity Recognition

The demand for security and user authentication has recently become an essential part of all aspects of our lives thus increasing the popularity of biometric recognition. As this technology becomes more common, the potential of spoofing attacks is of rising concern. For instance, conventional biometric traits, like face or fingerprints, are highly vulnerable to presentation attacks as these traits are exposed and can be easily replicated. This urges the scientific community to investigate other biometric modalities that are less prone to such attacks. Biomedical biometrics is an emerging technology that adopts vital signals acquired from the human body for biometric recognition. Such biosignals inherently provide liveliness detection and robustness to circumvention against presentation attacks as these modalities are difficult, if not impossible to replicate. Among various biomedical biometrics, the feasibility of the electrical activity of the brain, or Electroencephalogram (EEG), has been extensively examined for human identification tasks. This thesis proposes a new acquisition protocol to establish an EEG-based biometric system. The acquisition protocol adopts auditory stimulation to elicit a special class of brainwaves known as steady-state Auditory Evoked Potentials (AEP). AEPs, as neural responses share the same advantages of biomedical biometrics in terms of circumvention prevention and liveliness detection. Besides, steady-state AEPs share unique advantages that do not apply to other biosignals; being modular (i.e., stimulus-dependent) steady-state AEP supports cancellable biometrics, additionally, it can be linked to a user-selected PIN to implement a two-step authentication system. However, one of the most challenging aspects of deploying brainwaves in biometric systems is the high time-variability of the EEG signals. This study addresses this issue by investigating two different approaches: 1) exploiting BCI-spatial filtering techniques for target identification to maximize the intra-subject repeatability of the task-induced responses, 2) addressing inter-subject variability using a new training procedure for deep learning to improve the time-permanence of AEPs. Additionally, the feasibility of enhancing the collectability of EEG signals was also examined by reducing the acquisition time and the number of EEG electrodes. Overall, this thesis provides a framework that enhances the cross-session repeatability of brainwaves under auditory stimulation allowing the possibility of employing the AEP signals in biometric recognition.Ph.D